Identification of Temporally Varying Areas of Interest in Long-Duration Eye-Tracking Data Sets

• Published in: IEEE transactions on visualization and computer graphics Vol. 25; no. 1; pp. 87 - 97
• Main Authors: Muthumanickam, Prithiviraj K., Vrotsou, Katerina, Nordman, Aida, Johansson, Jimmy, Cooper, Matthew
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Air traffic control; areas of interest; Clustering; Complex systems; Domains; Emergency response; Eye-tracking data; Labeling; Merging; Microsoft Windows; minimum spanning tree; Monitoring; spatio-temporal data; Task analysis; temporal data; Tracking; Vehicles; Visualization
• ISSN: 1077-2626; 1941-0506; 1941-0506
• DOI: 10.1109/TVCG.2018.2865042

Eye-tracking has become an invaluable tool for the analysis of working practices in many technological fields of activity. Typically studies focus on short tasks and use static expected areas of interest (AoI) in the display to explore subjects' behaviour, making the analyst's task quite straightforward. In long-duration studies, where the observations may last several hours over a complete work session, the AoIs may change over time in response to altering workload, emergencies or other variables making the analysis more difficult. This work puts forward a novel method to automatically identify spatial AoIs changing over time through a combination of clustering and cluster merging in the temporal domain. A visual analysis system based on the proposed methods is also presented. Finally, we illustrate our approach within the domain of air traffic control, a complex task sensitive to prevailing conditions over long durations, though it is applicable to other domains such as monitoring of complex systems.